The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods.

In today’s digital world, the information systems are revolutionizing the way we connect. As the people are trying to adopt and integrate intelligent systems into daily lives, the risks around cyberattacks on user-specific information have significantly grown. To ensure safe communication, the Intrusion Detection Systems (IDS) were developed often by using machine learning (ML) algorithms that have the unique ability to detect malware against network security violations. Recently, it was reported that the IDS are prone to carefully crafted perturbations known as adversaries. With the aim to understand the impact of such attacks, in this paper, we have proposed a novel random neural network-based adversarial intrusion detection system (RNN-ADV). The NSL-KDD dataset is utilized for training. For adversarial attack crafting, the Jacobian Saliency Map Attack (JSMA) algorithm is used, which identifies the feature which can cause maximum change to the benign samples with minimum added perturbation. To check the effectiveness of the proposed adversarial scheme, the results are compared with a deep neural network which indicates that RNN-ADV performs better in terms of accuracy, precision, recall, F1 score and training epochs.

Little work has been reported on the magnitude and impact of interference with the performance of Internet of Things (IoT) applications operated by Long-Range Wide-Area Network (LoRaWAN) in the unlicensed 868 MHz Industrial, Scientific, and Medical (ISM) band. The propagation performance and signal activity measurement of such technologies can give many insights to effectively build long-range wireless communications in a Non-Line of Sight (NLOS) environment. In this paper, the performance of a live multi-gateway in indoor office site in Glasgow city was analysed in 26 days of traffic measurement. The indoor network performances were compared to similar performance measurements from outdoor LoRaWAN test traffic generated across Glasgow Central Business District (CBD) and elsewhere on the same LoRaWAN. The results revealed 99.95% packet transfer success on the first attempt in the indoor site compared to 95.7% at the external site. The analysis shows that interference is attributed to nearly 50 X greater LoRaWAN outdoor packet loss than indoor. The interference measurement results showed a 13.2–97.3% and 4.8–54% probability of interfering signals, respectively, in the mandatory Long-Range (LoRa) uplink and downlink channels, capable of limiting LoRa coverage in some areas.

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.